Using machine learning to examine street green space types at a high spatial resolution: Application in Los Angeles County on socioeconomic disparities in exposure

Sun, Yi; Wang, Xingzhi; Zhu, Jiayin; Chen, Liangjian; Jia, Yuhang; Lawrence, Jean M.; Jiang, Luohua; Xie, Xiaohui; Wu, Jun

doi:10.1016/j.scitotenv.2021.147653

Cited by 37 publications

(20 citation statements)

References 59 publications

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“…Due to the differences in the stages of urban development and the focus of ecological and environmental protection research at home and abroad, scholars at home and abroad have different definitions and understandings of green space. Foreign countries mostly refer to urban open space [10]. In 1877, the concept of urban open space was first put forward in London, England [11].…”

Section: Literature Reviewmentioning

confidence: 99%

Spatial and Temporal Evolution of Urban Green Space Pattern Based on GIS Sensors and Remote Sensing Information: Taking Xi’an as an Example

Wang

Zhang

et al. 2022

Journal of Sensors

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In order to solve the practical application of the continuously developing remote sensing technology in urban planning, this paper proposes a method of temporal and spatial evolution of urban green spatial pattern based on GIS remote sensing information. Based on the Landsat Image data of the main urban area of Xi’an from 2000 to 2012, different classification methods are used to extract the urban green space information and compare the accuracy. The classification results with high accuracy are selected to analyze the temporal and spatial evolution law of urban green space and the change of landscape pattern in the study area. In this paper, the change of vegetation coverage can be divided into five levels: significant degradation: <-0.006; slight degradation: -0.006~-0.002; stable: -0.002~0.002; slight improvement: 0.002~0.006; and significant improvement: >0.006. The results of this paper prove that this method can be used to understand and evaluate the ecological consequences of urbanization and improve our quality of life. At the same time, it can provide basic information for decision-making.

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Section: Literature Reviewmentioning

confidence: 99%

Spatial and Temporal Evolution of Urban Green Space Pattern Based on GIS Sensors and Remote Sensing Information: Taking Xi’an as an Example

Wang

Zhang

et al. 2022

Journal of Sensors

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“…We estimated green space exposure based on street view images within a 500 m radius surrounding the residential address at delivery 33,34 and obtained data from a validated machine-learning model developed in our prior study. 35,36 In brief, we collected street view images in Southern California from Microsoft Bing Maps Application Programming Interface for each street sampling location with an interval of 200 m. We then estimated the exposure to total green space and 3 different subtypes of green space (ie, trees, low-lying vegetation, and grass) by averaging the proportions of corresponding greenery pixels in all street view images within the 500 m buffer. 35,36…”

Section: Green Space Exposure Assessmentmentioning

confidence: 99%

Analysis of Heat Exposure During Pregnancy and Severe Maternal Morbidity

Jiao,

Sun,

Avila

et al. 2023

JAMA Netw Open

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ImportanceThe rate of severe maternal morbidity (SMM) is continuously increasing in the US. Evidence regarding the associations of climate-related exposure, such as environmental heat, with SMM is lacking.ObjectiveTo examine associations between long- and short-term maternal heat exposure and SMM.Design, Setting, and ParticipantsThis retrospective population-based epidemiological cohort study took place at a large integrated health care organization, Kaiser Permanente Southern California, between January 1, 2008, and December 31, 2018. Data were analyzed from February to April 2023. Singleton pregnancies with data on SMM diagnosis status were included.ExposuresModerate, high, and extreme heat days, defined as daily maximum temperatures exceeding the 75th, 90th, and 95th percentiles of the time series data from May through September 2007 to 2018 in Southern California, respectively. Long-term exposures were measured by the proportions of different heat days during pregnancy and by trimester. Short-term exposures were represented by binary variables of heatwaves with 9 different definitions (combining percentile thresholds with 3 durations; ie, ≥2, ≥3, and ≥4 consecutive days) during the last gestational week.Main Outcomes and MeasuresThe primary outcome was SMM during delivery hospitalization, measured by 20 subconditions excluding blood transfusion. Discrete-time logistic regression was used to estimate associations with long- and short-term heat exposure. Effect modification by maternal characteristics and green space exposure was examined using interaction terms.ResultsThere were 3446 SMM cases (0.9%) among 403 602 pregnancies (mean [SD] age, 30.3 [5.7] years). Significant associations were observed with long-term heat exposure during pregnancy and during the third trimester. High exposure (≥80th percentile of the proportions) to extreme heat days during pregnancy and during the third trimester were associated with a 27% (95% CI, 17%-37%; P &lt; .001) and 28% (95% CI, 17%-41%; P &lt; .001) increase in risk of SMM, respectively. Elevated SMM risks were significantly associated with short-term heatwave exposure under all heatwave definitions. The magnitude of associations generally increased from the least severe (HWD1: daily maximum temperature &gt;75th percentile lasting for ≥2 days; odds ratio [OR], 1.32; 95% CI, 1.17-1.48; P &lt; .001) to the most severe heatwave exposure (HWD9: daily maximum temperature &gt;95th percentile lasting for ≥4 days; OR, 2.39; 95% CI, 1.62-3.54; P &lt; .001). Greater associations were observed among mothers with lower educational attainment (OR for high exposure to extreme heat days during pregnancy, 1.43; 95% CI, 1.26-1.63; P &lt; .001) or whose pregnancies started in the cold season (November through April; OR, 1.37; 95% CI, 1.24-1.53; P &lt; .001).Conclusions and RelevanceIn this retrospective cohort study, long- and short-term heat exposure during pregnancy was associated with higher risk of SMM. These results might have important implications for SMM prevention, particularly in a changing climate.

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“…While normalized difference vegetation index (NDVI), a dimensionless measure that distinguishes between plant cover's reflectance in the visible and near-infrared, was substantially connected to GlobeLand30 green space (a typical dataset), NDWI was only modestly connected with it. The study [14] refers to an article that explores the relationships between socioeconomic (SES) & street green space elements within Los Angeles (LA) County, California. The photos of Microsoft Bing Maps combined with DL algorithm were utilized to assess overall categories of the street view green space, that were contrasted to the generally employed satellite-based green space metric also known as NDVI.…”

Section: Literature Reviewmentioning

confidence: 99%

Object-based Classification of Natural Scenes Using Machine Learning Methods

Jasim¹,

Younis²

2023

Technium

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The replication of human intellectual processes by machines, particularly computer systems, is known as artificial intelligence (AI). AI is an intelligent tool that is utilized across sectors to improve decision making, increase productivity, and eliminate repetitive tasks. Machine learning (ML) is a key component of AI since it includes understanding and developing ways that can learn or improve performance on tasks. For the last decade, ML has been applied in computer vision (CV) applications. In computer vision, systems and computers extract meaningful data from digital videos, photos, and other visual sources and use that information to conduct actions or make suggestions. In this work, we have solved the image segmentation problem for the natural images to segment out water, land, and sky. Instead of applying image segmentation directly to the images, images are pre-processed, and statistical and textural features are then passed through a neural network for the pixel-wise semantic segmentation of the images. We chose the 5X5 window over the pixel-by-pixel technique since it requires less resources and time for training and testing.

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Using machine learning to examine street green space types at a high spatial resolution: Application in Los Angeles County on socioeconomic disparities in exposure

Cited by 37 publications

References 59 publications

Spatial and Temporal Evolution of Urban Green Space Pattern Based on GIS Sensors and Remote Sensing Information: Taking Xi’an as an Example

Spatial and Temporal Evolution of Urban Green Space Pattern Based on GIS Sensors and Remote Sensing Information: Taking Xi’an as an Example

Analysis of Heat Exposure During Pregnancy and Severe Maternal Morbidity

Object-based Classification of Natural Scenes Using Machine Learning Methods

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